{"id":"W4388407541","doi":"10.3390/tomography9060162","title":"How Does Diagnostic Accuracy Evolve with Increased Breast MRI Experience?","year":2023,"lang":"en","type":"article","venue":"Tomography","topic":"MRI in cancer diagnosis","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Medicine; Medical diagnosis; Radiology; Breast MRI; Logistic regression; Biopsy; Magnetic resonance imaging; Pathological; Breast cancer; Cancer; Pathology; Internal medicine; Mammography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01155731,0.0003463601,0.0004617071,0.002066087,0.0003399822,0.002246853,0.001032494,0.00119127,0.00134894],"category_scores_gemma":[0.1052913,0.000379243,0.0007582974,0.001900132,0.001134441,0.002588041,0.00108738,0.0008469401,0.0007819952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007851573,"about_ca_system_score_gemma":0.0005482906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001971922,"about_ca_topic_score_gemma":0.001954434,"domain_scores_codex":[0.990347,0.003574265,0.001206203,0.001670406,0.002433737,0.0007683847],"domain_scores_gemma":[0.8734108,0.07407998,0.03700409,0.004777163,0.008258363,0.002469508],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005028191,0.000009055083,0.9935306,0.00001250783,0.00004104508,0.00005978937,0.00006448184,0.0001308874,0.00007346374,0.00002140883,0.0001117239,0.005894938],"study_design_scores_gemma":[0.000004374806,0.0001433448,0.9942726,0.0000429052,0.00006202435,0.001606494,0.0001967177,0.002426053,0.0004214403,0.0002436078,0.0005692492,0.00001114781],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9902309,0.002184702,0.002453692,0.001466715,0.00003839916,0.00001559432,0.0002583268,0.00006797122,0.003283736],"genre_scores_gemma":[0.998814,0.0002008755,0.0006092786,0.0001121398,0.00006463147,0.000004263656,0.0001180244,0.00001321545,0.00006359378],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01155731,"threshold_uncertainty_score":0.06112164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01125667518398039,"score_gpt":0.2620570642110753,"score_spread":0.2508003890270949,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}